Data-informed decision making:
Research data that can inform library
procurement and investment in the
student learning experience
By Yaz El Hakim and Emma Warren-Jones
• To consider the history and future of data within HE e.g. IoT
• Discuss the concept and benefit of Learner Analytics
• To outline and discuss some of the ethical considerations and
suggested good practice principles
• To collect some raw data and illustrate the insights that could
be drawn from these data at both the library and individual
Introduction and presentation of key concepts around learner analytics, big data and ethics.
The group will be asked to participate in the collection of some raw data in order to consider
ways in which currently uncollected and under-utilised data sets may facilitate future decisions
within the library and other departments. What may be defined as engagement, as opposed to
monitoring, will also be discussed in relation to the JISC publication (Sclater, 2014).
Discussion and Concluding points.
Code of practice for Learning Analytics
Responsibility of educational
institutions to ensure Learning
Analytics are conducted
responsibly, appropriately &
Transparency, privacy & validity of
Monitoring vs improving learning
So why are Learning Analytics
• Their value…
• Not in the money sense (that too) but in their ability to improve
the student’s learning experience.
• To realise the learning potential of a student within the context
of mass higher education, learning analytics can enhance the
quantity and depth of feedback that can be achieved by
• Students who had opportunities to seek and give peer
feedback were clearer of the expected standards and had
greater self-awareness of their performance
• What types of data do you currently collect in your libraries?
• What are you doing with these data? (analysed in isolation / in
• What decisions are these data being used to make?
• Are there better datasets that you could beneﬁt from?
• What are the datasets you use to currently make library investment
Types of data currently being
collected institutionally include:
What’s the problem with this data?
The Teaching Excellence Framework
• 12. After informal discussions with the sector, we believe at present there are three common
metrics (suitably benchmarked) that would best inform TEF judgements. We propose initially
to base the common metrics on existing data collections:
• Employment/destination: from the Destination of Leavers from Higher Education Surveys
(outcomes), and, from early 2017, make use of the results of the HMRC data match.
• Retention/continuation: from the UK Performance Indicators which are published by
Higher Education Statistics Agency (HESA) (outcomes)
• Student satisfaction indicators: from the National Student Survey (teaching quality and
NSSE is close, Attendance is closer, but
very little Actual Learning Data is utilised.
• Ironically, the library or more specifically, what the library provides and
how students engage with it is the central point of any degree and
arguably the most explicit learning data available!!!
• So why haven’t we been leading the way with real learning analytics …
More actual data that ﬂows into the Analytics
and Improve Predictive Modelling of the future.
Let’s collect some data:
1. Provide email addresses.
2. Add your favourite book to the RefME
project - ISBN is the most accurate source.
3. If you remember a speciﬁc quote – please
Actual Data Collection…
But the tradition of a degree is
that you read for it…
Therefore what you read matters
– even more importantly what
you cite, use, remember and
allow to inform and transform
your worldview, matters more!
Real Learning Data
Library resource management
Drive content usage (via RefME to discovery service)
Data Link Structure
Sign Ups Projects created References created
Field of study
RefME+ Institute dashboard
• Institutional libraries/librarians have a huge role to play in the leadership of
• Use of data is delicate and needs to be well prepared for, both in its
collection and utilisation.
• Development of reports and interventions at institutional and individual
levels will transform much of how we support learning and research
Data intelligence notes - estates management (2011), Available from:
https://www.hesa.ac.uk/intel?name=bds_emr [Accessed: 1.02.16].
King, J.H. & Richards, N.M. (2014) Big data ethics, Available from: http://
Kuh, G.D. (2003) What we’re learning about student engagement from
NSSE: Benchmarks for eﬀective educational practices, Change: The
Magazine of Higher Learning, 35, (2), 24–32.
Sclater, N. (2014) Code of practice for learning analytics A literature review
of the ethical and legal issues, http://repository.jisc.ac.uk/5661/1/